Survey data analysis pattern in Data Analysis Python

Introduction

Survey data analysis helps us understand what people think or feel by looking at their answers. It turns many answers into clear insights.

You want to find out which product feature customers like the most.

You need to summarize opinions from a customer satisfaction survey.

You want to compare responses from different groups, like age or location.

You want to see the most common answers to open questions.

You want to prepare a report showing survey results with charts.

Syntax

Data Analysis Python

import pandas as pd

# Load survey data
survey_df = pd.read_csv('survey.csv')

# Check basic info
survey_df.info()

# Summarize answers for a question
summary = survey_df['Question1'].value_counts()

# Calculate average rating
average = survey_df['Rating'].mean()

# Group by category and summarize
group_summary = survey_df.groupby('AgeGroup')['Rating'].mean()

Use value_counts() to count how many times each answer appears.

Use groupby() to analyze answers by different groups.

Examples

Count how many people chose each satisfaction level.

Data Analysis Python

summary = survey_df['Satisfaction'].value_counts()

Calculate the average age of survey participants.

Data Analysis Python

average_age = survey_df['Age'].mean()

Find the average rating given by each gender group.

Data Analysis Python

grouped = survey_df.groupby('Gender')['Rating'].mean()

Sample Program

This program creates a small survey dataset, counts how many people gave each satisfaction level, calculates the average rating overall, and then finds the average rating for each age group.

Data Analysis Python

import pandas as pd

# Create sample survey data
survey_data = {
    'AgeGroup': ['18-25', '26-35', '18-25', '36-45', '26-35', '18-25'],
    'Satisfaction': ['Good', 'Excellent', 'Good', 'Poor', 'Excellent', 'Good'],
    'Rating': [4, 5, 4, 2, 5, 3]
}

survey_df = pd.DataFrame(survey_data)

# Count satisfaction answers
satisfaction_counts = survey_df['Satisfaction'].value_counts()

# Calculate average rating overall
average_rating = survey_df['Rating'].mean()

# Calculate average rating by age group
rating_by_age = survey_df.groupby('AgeGroup')['Rating'].mean()

print('Satisfaction counts:')
print(satisfaction_counts)
print('\nAverage rating overall:')
print(round(average_rating, 2))
print('\nAverage rating by age group:')
print(rating_by_age)

Important Notes

Always check your data for missing or wrong answers before analysis.

Use rounding to make average numbers easier to read.

Grouping helps compare different segments in your survey.

Summary

Survey data analysis turns many answers into clear counts and averages.

Use value_counts() for counting answers and groupby() for comparing groups.

Simple statistics like averages help summarize ratings or scores.

Practice

1. What does the value_counts() function do when analyzing survey data in Python?

easy

A. Calculates the average of numerical answers

B. Groups data by categories for comparison

C. Counts how many times each unique answer appears

D. Removes missing values from the dataset

Solution

1. Step 1: Understand the purpose of value_counts()

   This function counts the frequency of each unique value in a column, showing how many times each answer appears.

2. Step 2: Compare with other functions

   Calculating averages is done by mean(), grouping is done by groupby(), and removing missing values uses dropna().

3. Final Answer:

   Counts how many times each unique answer appears -> Option C

4. Quick Check:

   value_counts() = count unique answers [OK]

Hint: Remember: value_counts counts answers, not averages [OK]

Common Mistakes:

• Confusing value_counts with mean()
• Thinking value_counts groups data
• Assuming it removes missing data

2. Which of the following is the correct syntax to get the average rating from a survey column named rating in a pandas DataFrame df?

easy

A. df['rating'].mean()

B. df['rating'].count()

C. df.groupby('rating').sum()

D. df['rating'].value_counts()

Solution

1. Step 1: Identify the function for average calculation

   The mean() function calculates the average of numerical values in a column.

2. Step 2: Check other options

   count() counts non-missing values, groupby().sum() sums grouped data, and value_counts() counts unique values.

3. Final Answer:

   df['rating'].mean() -> Option A

4. Quick Check:

   Average = mean() [OK]

Hint: Use mean() to get average of numeric columns [OK]

Common Mistakes:

• Using count() instead of mean()
• Confusing value_counts() with average
• Trying to sum without grouping

3. Given the following code, what will be the output?

import pandas as pd
data = {'gender': ['M', 'F', 'F', 'M', 'F'], 'score': [5, 3, 4, 2, 5]}
df = pd.DataFrame(data)
result = df.groupby('gender')['score'].mean()
print(result)

medium

A. gender
F 3.5
M 4.0
Name: score, dtype: float64

B. gender
F 4.0
M 3.5
Name: score, dtype: float64

C. gender
M 4.0
F 3.5
Name: score, dtype: float64

D. gender
F 3.0
M 4.0
Name: score, dtype: float64

Solution

1. Step 1: Group scores by gender

   Group 'score' values by 'gender': For 'F' scores are [3,4,5], for 'M' scores are [5,2].

2. Step 2: Calculate mean for each group

   Mean for 'F' = (3+4+5)/3 = 4.0; Mean for 'M' = (5+2)/2 = 3.5.

3. Final Answer:

   gender F 4.0 M 3.5 Name: score, dtype: float64 -> Option B

4. Quick Check:

   Group mean = correct averages [OK]

Hint: Calculate group means by averaging grouped values [OK]

Common Mistakes:

• Mixing up group labels and values
• Calculating sums instead of means
• Ignoring group sizes

4. The following code is intended to count how many times each answer appears in the 'response' column, but it raises an error. What is the error and how to fix it?

import pandas as pd
df = pd.DataFrame({'response': ['Yes', 'No', 'Yes', 'Maybe']})
counts = df.response.value_count()
print(counts)

medium

A. KeyError because 'response' column does not exist

B. SyntaxError due to missing parentheses in print statement

C. TypeError because value_count() requires an argument

D. AttributeError because the method name should be value_counts()

Solution

1. Step 1: Identify the method name error

   The method value_count() is incorrect; the correct method is value_counts() with an 's' at the end.

2. Step 2: Correct the method name

   Changing value_count() to value_counts() fixes the AttributeError and allows counting unique values.

3. Final Answer:

   AttributeError because the method name should be value_counts() -> Option D

4. Quick Check:

   Correct method is value_counts() [OK]

Hint: Remember the method ends with 's': value_counts() [OK]

Common Mistakes:

• Misspelling method name without 's'
• Confusing print syntax errors
• Assuming column name is missing

5. You have a survey DataFrame df with columns age_group and satisfaction_score. How would you calculate the average satisfaction score for each age group and then find which age group has the highest average score?

hard

A. Use df.groupby('age_group')['satisfaction_score'].mean().idxmax()

B. Use df['satisfaction_score'].max() directly

C. Use df.value_counts('age_group') and pick max

D. Use df.groupby('satisfaction_score')['age_group'].mean()

Solution

1. Step 1: Group by age group and calculate average satisfaction

   Use groupby('age_group')['satisfaction_score'].mean() to get average scores per group.

2. Step 2: Find the age group with the highest average

   Use idxmax() on the resulting Series to get the group label with the highest average score.

3. Final Answer:

   Use df.groupby('age_group')['satisfaction_score'].mean().idxmax() -> Option A

4. Quick Check:

   Group mean + idxmax() finds highest average [OK]

Hint: Chain mean() and idxmax() to find top group quickly [OK]

Common Mistakes:

• Using max() on scores without grouping
• Confusing value_counts() with averages
• Grouping by wrong column